拉伸指数模型DWI鉴别诊断乳腺良恶性病变

Stretched-exponential model of DWI in differentiating malignant and benign breast lesions

目的探讨拉伸指数模型DWI鉴别诊断乳腺良恶性病变的价值。方法收集58例乳腺病变患者,共63个病灶(良性33个,恶性30个),行多b值DWI及动态增强MRI(DCE-MRI)扫描。计算ADC、扩散分布指数(DDC)和扩散异质性指数(α)值,并生成时间-信号强度曲线(TIC)。比较良恶性病变间各参数差异,采用ROC曲线评价各参数诊断效能。结果恶性病变ADC、DDC和α分别为(1.01±0.19)×10^(-3) mm^2/s、(0.89±0.23)×10^(-3) mm^2/s和0.75±0.09,良性病变分别为(1.41±0.27)×10^(-3) mm^2/s、(1.49±0.29)×10^(-3) mm^2/s和0.87±0.07,恶性病变均低于良性病变(P均<0.01)。各参数中DDC曲线下面积(AUC)最大(AUC=0.958),最佳诊断界值1.22×10^(-3) mm^2/s,敏感度和特异度分别为96.67%、81.82%,DDC与TIC联合所得AUC为0.976,对应敏感度和特异度分别为93.33%、93.94%。结论拉伸指数模型DWI参数DDC、α能够鉴别诊断乳腺良恶性病变,DDC与TIC联合的诊断效能高于ADC和DCE。

Objective To investigate the value of stretched-exponential model of DWI in differential diagnosis of benign and malignant breast lesions.Methods Totally 58 patients with 63 breast lesions（33 benign,30 malignant lesions）were enrolled.All the patients underwent multiple-b-value DWI and dynamic contrast enhancement MRI（DCE-MRI）scans.The values of ADC,DDC and water molecular diffusion heterogeneity index（α）were calculated,and the time-signal intensity curve（TIC）was obtained.All the parameters were compared between benign and malignant breast lesions.The diagnostic performance of different parameters was evaluated with ROC curve.Results ADC,DDC andαvalue of malignant lesions was（1.01±0.19）×10-3 mm2/s,（0.89±0.23）×10-3 mm2/s and 0.75±0.09,while of benign lesions was（1.41±0.27）×10-3 mm2/s,（1.49±0.29）×10-3 mm2/s and 0.87±0.07,respectively.All 3 parameters in malignant lesions were lower than those in benign lesions（all P〈0.01）.Taking 1.22×10-3 mm2/s as the optimal threshold,the area under the curve（AUC）of DDC was the largest as 0.958,and the corresponding diagnostic sensitivity and specificity was 96.67% and 81.82%,respectively.AUC value was 0.976 by combining DDC with TIC,and the corresponding diagnostic sensitivity and specificity was 93.33% and 93.94%,respectively.Conclusion The stretchedexponential model DWI can differentiate breast lesions,and diagnostic performance of combination of DDC and TIC is better than ADC or DCE.